OPTIMAL INSURANCE STRATEGIES: A HYBRID DEEP LEARNING MARKOV CHAIN APPROXIMATION APPROACH
Xiang Cheng, Zhuo Jin, Hailiang Yang
ASTIN Bulletin | Cambridge University Press (CUP) | Published : 2020
This paper studies deep learning approaches to find optimal reinsurance and dividend strategies for insurance companies. Due to the randomness of the financial ruin time to terminate the control processes, a Markov chain approximation-based iterative deep learning algorithm is developed to study this type of infinite-horizon optimal control problems. The optimal controls are approximated as deep neural networks in both cases of regular and singular types of dividend strategies. The framework of Markov chain approximation plays a key role in building the iterative equations and initialization of the algorithm. We implement our method to classic dividend and reinsurance problems and compare th..View full abstract
Awarded by Research Grants Council of the Hong Kong Special Administrative Region
We are grateful to the editors and anonymous referees for their insightful comments and suggestions. These comments/suggestions greatly improved the quality and readability of the paper. This research was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region (project no. 17330816) and by a Faculty Research Grant from The University of Melbourne.